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Deep Learning to Optimize: Security-Constrained Unit Commitment With Uncertain Wind Power Generation and BESSs
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DOI：10.1109/TSTE.2021.3107848.png)
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Abstract
En 中文
This paper proposes a new model of scenario-based security-constrained unit commitment (SCUC) with BESSs. By formulating such a model as a mixed-integer programming (MIP) problem, we can obtain the optimal control strategy of units and BESSs to reduce the operating cost. To solve this MIP with the proposed model, we propose a new learning-based approach to tackle the SCUC problem. The proposed convolutional neural network (CNN)-based SCUC algorithm (CNN-SCUC) has two main stages. First, CNN-SCUC trains a CNN to obtain solutions to the binary variables corresponding to unit commitment decisions. Then, the continuous variables corresponding to unit power outputs are solved by a small-scale convex optimization problem. In contrast to existing work, CNN-SCUC eliminates the need of explicitly considering the scenario-based security constraints in the optimization problem, and thus greatly reduces the computational complexity. The average gap to the optimal solution is as small as 0.0267%. The algorithm is also scalable in the sense that the computational time is reduced from about 1236.32 seconds to 0.8379 seconds in a 10-unit and 200-scenario system. Besides, the computation time remains almost constant when the number of scenarios increases. Case studies show that compared with the traditional scenario-based SCUC model, more than 4.70% operating cost reduction is achieved by incorporating BESSs in the system.
Keywords:
Convolutional neural networks
Wind power generation
Matrix decomposition
Security
Upper bound
Uncertainty
State of charge
Deep learning
security-constrained unit commitment
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